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Philippines

Is Public Transport Affordable?

Julie Babinard's picture
When planning transport systems in developing countries, one of the main challenges is to evaluate the proportion of income spent by poorer households on transport as well as in understanding transport patterns in relation to residential location, travel distance and travel mode. High real estate prices in urban centers often force low-income households in developing countries to live farther out in the periphery, with consequences on the way urban agglomerations develop and with subsequent effects on the levels of motorization, congestion, local air pollution, physical activity and the expansion of urban poverty.

Open Data + Urban Transport = ?

Holly Krambeck's picture

For fun, suppose you were a software developer, and you came up with a terrific idea to communicate public transit information. For example, imagine your city experiences frequent floods, and you have devised an automated system that sends SMS texts to passengers, advising them of alternative transit routes during emergencies.

How much revenue do you think you could earn for that software? How many people could you positively impact?

 

What if I told you that today, by taking advantage of one tiny revolution in open data, you could take those numbers and multiply them by 350, turning $100,000 into $35 million, or 1 million people into 350 million? Sounds pretty good, right? If you are in international development, sounds like a promotion…

The 10-Cent GPS

Holly Krambeck's picture

We know that technology is not a panacea, that gadgetry and software are not always the right solutions for our transport problems. But how do we know – really know -- when technology is truly the wrong way to go – when, say, using an old-fashioned compass is genuinely better than a GPS?

Thanks to blogger Sebastiao Ferreira, writing for MIT’s CoLab Radio, I have learned about an intriguing phenomenon in Lima, where entrepreneur data collectors, named dateros, stand with clipboards along frequented informal microbus routes, collecting data on headways, passenger counts, and vehicle occupancy levels. The microbus drivers pay dateros about 10-cents per instant update, and they use the information to adjust their driving speed.  For example, if there is a full bus only a minute ahead of the driver’s vehicle, the driver will slow down, hoping to collect more passengers further down the route. In informal transit systems, where drivers’ incomes are directly tied to passenger counts, paying dateros is a good investment (Photo from MIT CoLab Radio).

If you think about it, use of dateros could be more efficient than traditional schedule or GPS-based dispatch, because the headways are dynamically and continuously updated to optimize the number of passengers transported at any given time of day.  According to Jeff Warren (a DIY cartography pioneer), the dateros have been praised as the “natural database, an ‘informal bank’ of transportation optimization data.”

Does this little-known practice call into question our traditional prescription for high-tech solutions to bus dispatch?